Department of Animal and Dairy Science, University of Georgia, Athens, GA.
Genus PIC, Hendersonville, TN.
J Anim Sci. 2019 Apr 3;97(4):1513-1522. doi: 10.1093/jas/skz042.
Genomic selection (GS) is routinely applied to many purebreds and lines of farm species. However, this method can be extended to predictions across purebreds as well as for crossbreds. This is useful for swine and poultry, for which selection in nucleus herds is typically performed on purebred animals, whereas the commercial product is the crossbred animal. Single-step genomic BLUP (ssGBLUP) is a widely applied method that can explore the recently developed algorithm for proven and young (APY). The APY allows for greater efficiency in computing BLUP solutions by exploiting the theory of limited dimensionality of genomic information and chromosome segments (Me). This study investigates the predictivity as a proxy for accuracy across and within 2 purebred pig lines and their crosses, under the application of APY in ssGBLUP setup, and different levels of Me overlapping across populations. The data consisted of approximately 210k phenotypic records for 2 traits (T1 and T2) with moderate heritability. Genotypes for 43k SNP markers were available for approximately 46k animals, from which 26k and 16k belong to 2 pure lines (L1 and L2), and approximately 4k are crossbreds. The complete pedigree had more than 720k animals. Different multivariate ssGBLUP models were applied, either with the regular or APY inverse of the genomic relationship matrix (G). The models included a standard bivariate animal model with 3 lines evaluated as 1 joint line, and for each trait individually, a 3-trait animal model with each line treated as a separate trait. Both models provided the same predictivity across and within the lines. Using either of the pure lines data as a training set resulted in a similar predictivity for the crossbreed animals (0.18 to 0.21). Across-line predictive ability was limited to less than half of the maximum predictivity for each line (L1T1 0.33, L1T2 0.25, L2T1 0.35, L2T2 0.36). For crossbred predictions, APY performed equivalently to regular G inverse when the number of core animals was equal to the number of eigenvalues explaining between 98% and 99% of the variance of G (4k to 8k) including all lines. Predictivity across the lines is achievable because of the shared Me between them. The number of those shared segments can be obtained via eigenvalue decomposition of genomic information available for each line.
基因组选择（GS）通常应用于许多纯种和农场动物的品系。然而，这种方法也可以扩展到纯种之间的预测，以及杂交种的预测。这对于猪和家禽非常有用，因为在核心群体中进行选择通常是针对纯种动物进行的，而商业产品是杂交动物。一步法基因组 BLUP（ssGBLUP）是一种广泛应用的方法，可以探索最近开发的用于证明和年轻（APY）的算法。APY 通过利用基因组信息和染色体片段的有限维理论（Me），在计算 BLUP 解时可以提高效率。本研究在应用于 ssGBLUP 设定的 APY 以及不同种群之间 Me 重叠水平下，调查了两个纯种猪系及其杂交种之间和内部的预测能力作为准确性的替代指标，两个性状（T1 和 T2）的表型记录约为 21 万条，遗传力适中。大约 46000 只动物的 43000 个 SNP 标记的基因型可用，其中 26000 个和 16000 个属于两个纯种系（L1 和 L2），大约 4000 个是杂交种。完整的系谱中有超过 72 万只动物。应用了不同的多元 ssGBLUP 模型，要么使用常规的基因组关系矩阵（G）的逆，要么使用 APY 逆。模型包括一个标准的三线性动物模型，3 个系作为 1 个联合系进行评估，对于每个性状，分别是 3 个性状动物模型，每个系作为单独的性状处理。这两个模型在系内和系间都提供了相同的预测能力。使用任何一个纯种系的数据作为训练集，对杂交动物的预测结果相似（0.18 到 0.21）。跨系预测能力仅限于每个系最大预测能力的一半以下（L1T1 0.33，L1T2 0.25，L2T1 0.35，L2T2 0.36）。对于杂交种的预测，当核心动物的数量等于解释 G 方差的 98%到 99%的特征值数量（4000 到 8000）时，APY 与常规 G 逆的表现相当，包括所有系。由于它们之间共享 Me，因此可以实现跨系的预测能力。可以通过对每个系可用的基因组信息进行特征值分解来获得这些共享片段的数量。
